{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 获取售房时间、平均房价两列的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      tradeTime   price\n",
      "0      2016-8-9   31680\n",
      "1     2016-7-28   43436\n",
      "2    2016-12-11   52021\n",
      "3     2016-9-30   22202\n",
      "4     2016-8-28   48396\n",
      "..          ...     ...\n",
      "506   2016-7-21   17935\n",
      "507   2016-7-16  146161\n",
      "508    2016-9-9   28682\n",
      "509  2016-12-31   24648\n",
      "510   2016-7-31   52656\n",
      "\n",
      "[511 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#打开csv文件\n",
    "df=pd.read_csv(\"C:/Users/3SXL_21148/Desktop/data.csv\",header=0,sep=\",\",engine=\"python\")\n",
    "#获取“tradeTime”，“price”两列值\n",
    "data=df.loc[:,[\"tradeTime\",\"price\"]]\n",
    "\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 按照售房时间进行排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     tradeTime  price\n",
      "360 2016-06-16  36431\n",
      "134 2016-06-16  15991\n",
      "138 2016-06-16  70738\n",
      "343 2016-06-16  23900\n",
      "284 2016-06-17  29127\n",
      "..         ...    ...\n",
      "239 2017-03-05  30688\n",
      "140 2017-03-09  39692\n",
      "60  2017-03-22  67470\n",
      "454 2017-03-24  61685\n",
      "166 2017-04-13  56900\n",
      "\n",
      "[511 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "data[\"tradeTime\"]=pd.to_datetime(data[\"tradeTime\"])\n",
    "data.sort_values(by=\"tradeTime\",ascending=True,inplace=True)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 属性规约，将时间年月日改成年月"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     price tradeTime\n",
      "360  36431    2016-6\n",
      "134  15991    2016-6\n",
      "138  70738    2016-6\n",
      "343  23900    2016-6\n",
      "284  29127    2016-6\n",
      "..     ...       ...\n",
      "239  30688    2017-3\n",
      "140  39692    2017-3\n",
      "60   67470    2017-3\n",
      "454  61685    2017-3\n",
      "166  56900    2017-4\n",
      "\n",
      "[511 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "time_list=[]\n",
    "#属性规约\n",
    "for i in range(len(data)):\n",
    "    data_year=data.iloc[i,0].year\n",
    "    data_month=data.iloc[i,0].month\n",
    "    data_year_month=str(data_year)+\"-\"+str(data_month)\n",
    "    time_list.append(data_year_month)\n",
    "#删除原来tradetime列\n",
    "data=data.drop([\"tradeTime\"],axis=1)\n",
    "#添加新的tradeTime列\n",
    "data.loc[:,\"tradeTime\"]=time_list\n",
    "    \n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4进行统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             price\n",
      "tradeTime         \n",
      "2016-10    46949.0\n",
      "2016-11    55324.0\n",
      "2016-12    50221.0\n",
      "2016-6     39825.0\n",
      "2016-7     41205.0\n",
      "2016-8     44251.0\n",
      "2016-9     43527.0\n",
      "2017-1     56734.0\n",
      "2017-2     54203.0\n",
      "2017-3     49884.0\n",
      "2017-4     56900.0\n"
     ]
    }
   ],
   "source": [
    "data=data.groupby(\"tradeTime\").mean()\n",
    "\n",
    "data[\"price\"]=data[\"price\"].round(0)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 画折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8f19f70>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
